5 papers
Towards Robust Version Identification in the Wild: A Dataset, Benchmark, and Fine-Tuning Study
Simon Hachmeier, R. Oguz Araz, Dmitry Bogdanov +2
Existing datasets for musical version identification (VI) are primarily derived from curated metadata sources such as SecondHandSongs and Discogs, and are therefore dominated by pr…
OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
Pablo Alonso-Jiménez, Pedro Ramoneda, R. Oguz Araz +2
Developing open-source foundation models is essential for advancing research in music audio understanding and ensuring access to powerful, multipurpose representations for music in…
Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification
R. Oguz Araz, Guillem Cortès-SebastiÃ, Emilio Molina +4
Audio fingerprinting (AFP) allows the identification of unknown audio content by extracting compact representations, termed audio fingerprints, that are designed to remain robust a…
Supervised contrastive learning from weakly-labeled audio segments for musical version matching
Joan SerrÃ, R. Oguz Araz, Dmitry Bogdanov +1
Detecting musical versions (different renditions of the same piece) is a challenging task with important applications. Because of the ground truth nature, existing approaches match…
Discogs-VI: A Musical Version Identification Dataset Based on Public Editorial Metadata
R. Oguz Araz, Xavier Serra, Dmitry Bogdanov
Current version identification (VI) datasets often lack sufficient size and musical diversity to train robust neural networks (NNs). Additionally, their non-representative clique s…